Top 10 data mining boot camp 2022

Data mining boot camp:

• WeCloudData.

• SIT Academy.

• CodingNomads.

• Practicum.

• Data Science Dojo.

• Jedha.

• Coding Temple.

• The Dev Masters.

• Metis

• RMOTR

What is data mining?

Easy way to understand data mining:

Data mining is the process of mining and discovering patterns in large data sets involving the intersection of machine learning, statistics, and database systems.

 Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from data sets and transforming the information into a simple structure for use.

 Data mining is the "knowledge discovery in the database" process or the analysis step of KDD. In addition to the analysis step, it includes aspects of database and data management, data pre-processing, model and hypothesis consideration, attractiveness metrics, complexity consideration, post-processing of discovered structures, visualization and online updates.

How Data Mining Works

How does data mining works:

Pre-processing or data preparation

Before using the data mining algorithm, a target data set must be assembled. That is, combining the data that will be mined. Since data mining can only uncover patterns present in the data, the stored data set must be large enough to contain these patterns and short enough to be mined in an acceptable time frame. A common source of data is a data mart or data warehouse Pre-processing or data preparation is essential for analyzing multivariate data sets before data mining. Data sets stored in this way are mined to remove redundant data.

Data mining

Data mining involves six general classes of tasks:

1. Abnormality Detection:   Saves essential data by detecting abnormal data records. Avoid or remove unnecessary data.

2. Association rule learning (dependency modeling ): explores relationships between variables. For example, a supermarket may collect information on a customer's purchasing habits. This method is also sometimes used in markets.

3. Clustering : Organizes the stored data according to structure.

4. Classification: divides new data into valid and invalid categories. For example, an e-mail program classifies an e-mail as "legitimate" or "spam".

5. Regression : Attempts to find a function that can model the data with perfect accuracy to estimate relationships between data or datasets.

6. Compaction : Provides a more compact representation of data sets, including visualization and report generation.

Validity of results

Validate whether the results are correct or not after the final data mining.

Data mining can be unintentionally misused, producing results that appear significant but that do not actually predict future behavior and cannot be reproduced in a new sample of data, so are of little use. This is sometimes caused by investigating too many hypotheses and not testing the correct statistical hypothesis. A common version of this problem in machine learning is known as overfitting. To overcome this problem the validity of the results is checked.

Applied filed of data mining

Currently data mining is being used in various sectors, some of the notable ones are given below.

 media and technologies

There is no alternative to data mining to survive in the competitive market. Various media and technology companies use analytical models to understand customer data, to offer products or services tailored to their customers' needs.

Insurance

With analytical knowledge, insurance companies use data mining to solve complex problems related to fraud, compliance, risk management and customer attrition.

education

With a unified, data-driven view of student progress, educators can predict student performance before they even step foot in the classroom – and fine-tune their teaching strategies by mining the data.

Manufacturing

Aligning supply plans with demand forecasts is essential, as is early detection of issues, quality assurance, and investment in brand equity. Data mining can play an important role in solving these problems.

Why learn data mining?

Businesses build large databases of consumer data that they use to size and focus their marketing efforts. Businesses need ways to manage and use this data to develop targeted, personalized marketing communications. Data mining helps businesses understand consumer behavior and generate new customers.

If you are a businessman or want to work or freelancing then you must learn data mining. A new trader can easily achieve success by using data mining. Because if the organization analyzes the market data and provides products or services knowing the tastes and interests of the fans, then there is no question of not being successful.

How to Learn Data Mining

data mining courses:

Data mining is a complex subject. You can't learn this easily. Edx is the best among which you can take free data mining course through various channels.

Take free online data mining courses to build your skills and advance your career. You can also learn the basics of data mining through YouTube.

To learn data mining well you need to take data mining premium course. Generally, free courses cover the basics of data mining.

Before learning data mining you need to know programming.